title: Retinal vessel classification: sorting arteries and veins.
abstract: For the discovery of biomarkers in the retinal vasculature it is essential to classify vessels into arteries and veins. We automatically classify retinal vessels as arteries or veins based on colour features using a Gaussian Mixture Model, an Expectation-Maximization (GMM-EM) unsupervised classifier, and a quadrant-pairwise approach. Classification is performed on illumination-corrected images. 406 vessels from 35 images were processed resulting in 92% correct classification (when unlabelled vessels are not taken into account) as compared to 87.6%, 90.08%, and 88.28% reported in [12] [14] and [15]. The classifier results were compared against two trained human graders to establish performance parameters to validate the success of classification method. The proposed system results in specificity of (0.8978, 0.9591) and precision (positive predicted value) of (0.9045, 0.9408) as compared to specificity of (0.8920, 0.7918) and precision of (0.8802, 0.8118) for (arteries, veins) respectively as reported in [13]. The classification accuracy was found to be 0.8719 and 0.8547 for veins and arteries, respectively.
authors.forename: D
authors.initials: D
authors.lastname: Relan
authors.forename: T
authors.initials: T
authors.lastname: MacGillivray
authors.forename: L
authors.initials: L
authors.lastname: Ballerini
authors.forename: E
authors.initials: E
authors.lastname: Trucco
mesh_terms: D000465:Algorithms; D001158:Arteries / Q000473:pathology*; D015415:Biomarkers; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012160:Retina; D012171:Retinal Vessels / Q000473:pathology*; D012680:Sensitivity and Specificity; D012984:Software; D014680:Veins / Q000473:pathology*
chemical_list: D015415:Biomarkers